Error Handling & Graceful Degradation

AI systems fail in ways that are fundamentally different from traditional software. A conventional application either works or crashes - the failure is obvious. AI can fail subtly, producing outputs that look perfectly reasonable but are completely wrong. This makes error handling especially challenging. You can't rely on users to notice errors, because the errors don't look like errors. Graceful degradation - the ability to continue functioning at a reduced level when something goes wrong - takes on new meaning with AI. It might mean falling back to a simpler model when the primary one is uncertain, flagging outputs that fall outside normal confidence ranges, or routing to human review when the system encounters inputs that are unlike its training data. Good error handling in AI systems also means being honest when the system can't help. A response of "I'm not confident enough to answer this - here's what I'd suggest instead" is far more valuable than a plausible-sounding but unreliable answer. Designing these fallback paths requires understanding not just the technical failure modes but how users experience and respond to different types of failure.